Memristive System Research Articles

Coexistence of hidden attractors in memristive chaotic system

In this paper, a charge controlled memristor model is introduced into the Sprott-A system equation to construct a new memristor chaotic system and the calculation of this new system satisfies the characteristics of no equilibrium points. The periodic function is added to the new constructed memristor chaotic system, and the coexistence of attractors in memristor chaotic system without equilibrium points is obtained by adjusting the control parameters. Through different analytical methods to explore the characteristics of the new system. The dynamic behaviors of the system before and after the periodic transformation are compared and analyzed. In the end, DSP simulation is used to verify the feasibility of the theoretical model. The coexistence of attractors in memristor chaotic systems can improve the flexibility and security of chaotic encryption systems. Further research on this kind of phenomena can meet the needs of higher encryption.

Design and FPGA realization of memristive multibutterfly chaotic system featuring coexisting attractors and complex multistability behavior

Design and FPGA realization of memristive multibutterfly chaotic system featuring coexisting attractors and complex multistability behavior

Exponential Synchronization Control of Reaction-Diffusion Fuzzy Memristive Neural Networks: Hardy-Poincarè Inequality.

This article is devoted to solving the exponential synchronization problem of a new type of fuzzy memristive neural network with reaction-diffusion terms. By introducing adaptive laws, two controllers are designed. After combining the inequality technique with the Lyapunov function approach, some easily verified sufficient conditions are established to ensure the exponential synchronization of the reaction-diffusion fuzzy memristive system under the proposed adaptive scheme. In addition, by using the Hardy-Poincarè inequality, the diffusion terms are estimated associated with the information of the reaction-diffusion coefficients and the regional feature, which improves some existing conclusions. Finally, an illustrative example is presented to demonstrate the validity of the theoretical results.

Interior Crisis Route to Extreme Events in a Memristor-Based 3D Jerk System

In dynamical systems, events that deviate significantly from usual or expected behavior are referred to as extreme events. This paper investigates the mechanism of extreme event generation in a 3D jerk system based on a generalized memristive device. In addition, regions of coexisting parallel bifurcation branches are explored as a way of investigating the multistability of the memristive system. The system is examined using bifurcation diagrams, Lyapunov exponents, time series, probability density functions of events, and inter-event intervals. It is found that extreme events occur via a period-doubling route and are due to an interior crisis that manifests itself as a sudden shift from low-amplitude to high-amplitude oscillations. Multistability is also identified when both control parameters and initial values are modified. Finally, an analog circuit based on the memristive jerk system is designed and experimentally realized. To our knowledge, this is the first time that extreme events have been reported in a memristive jerk system in particular and in jerk systems in general.

An image encryption scheme based on an improved memristive neuron chaotic system

With fast-developing Internet and communication, the security transmission of image in network has become a research highlight. So far, researchers have designed a lot of image encryption methods based on chaotic models, some of them are not secure enough. To enhance security of image transmission on the network, in this paper, an image encryption method is developed from a 3D memeristive FitzHugh-Nagumo (FHN) neuron. First, a 3D memeristive FHN model is obtained by connecting a memeristor into a 2D FHN model, and dynamics for 3D FHN model are estimated by applying phase diagrams, bifurcation and Lyapunov exponent. Then, an image encryption algorithm is proposed by using this 3D model. Finally, security of encryption algorithm is estimated. Simulation results confirm the effectiveness of encryption scheme.

Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks

AbstractThe synaptic plasticity of the Ag‐Ag2S nanoparticle‐based volatile memristor system is demonstrated. The nanoparticles self‐assemble into a network with over 103 interconnected atomic switch interfaces. Short‐term plasticity is identified by spontaneous conductance relaxation, attributed to the memristor's volatility. The conductance of the network is enhanced when a subsequent stimulus pulse arrives shortly after the previous one, analogous to the paired‐pulse facilitation in biological synapses. Furthermore, repeated pulse stimulation is used to achieve the transition from short‐term plasticity to long‐term potentiation, a process related to learning and memory formation. Remarkably, the result reveals that the lifetime of long‐term potentiation for 100‐pulse stimulation is 40 min, indicating that the device can forget newly acquired information after prolonged storage, akin to human memories. The findings provide insight into the the learning and memory abilities of atomic switch network memristors, facilitating the development of hardware‐implemented artificial neural networks.

A new four-valued memristive hyperchaotic system with hidden attractors and multistability

A four-valued memristor is designed based on the memristor model and introduced into a three-dimensional chaotic system to construct a four-dimensional four-valued memristive chaotic system, and its dynamics are investigated and discussed. The properties of the system such as phase diagrams, Poincaré map, dissipation, equilibrium points stability, Lyapunov exponents, Lyapunov dimensions, dynamical complexity, offset boosting, inversion properties, and attractor coexistence are investigated. The system is found to have infinitely many equilibrium points with hidden attractors, and has higher dimensionality, and complexity than the original system. As the parameters change, the new system transitions from a quasi-periodic state to a chaotic state to a hyperchaotic state and finally back to a quasi-periodic state through a reverse period-doubling bifurcation. The complexity of the system is verified using the C0 algorithm and the spectral entropy algorithm. The offset boosting control of the system can be realized by adding parameters to the system. The system also has phase reversal characteristics of a single parameter control system attractor, and there are multiple types of attractors coexisting with changes in the system parameters at different initial values. In addition, when the parameters of the system are fixed, the existence of exterme multistability based on the initial value of w0 the memristor is found. Finally, the feasibility of the design and the correctness of the analysis are verified by simulated circuits. Therefore, the system is highly complex and can be applied in the fields of image encryption and confidential communication.

A novel memristive chaotic jerk circuit and its microcontroller-based sliding mode control

Due to the experimental realization of memristor circuit elements, research on memristors and memristor-based circuits has surged. Because of their nonvolatile and nonlinear behavior, memristors can be easily applied to chaotic circuits. This study introduces a novel memristive 3D chaotic jerk system, comprising only seven terms, along with its electronic model and microcontroller-based control. The flux-controlled memristor-based jerk system exhibits complex dynamics, which were analyzed through various properties such as phase portraits, the Jacobian matrix, equilibria, eigenvalues, Lyapunov spectra, bifurcation diagrams, and transient chaos behavior. Three controllers, namely, nonlinear feedback, classical sliding mode, and integral sliding mode were designed to control the chaotic jerk oscillator. Lyapunov functions were used to synthesize the nonlinear feedback controller and ensure system stability with the sliding mode technique. Numerical tests under various performance criteria and disturbance conditions showed that the sliding mode controller outperforms the nonlinear feedback controller due to its single-state control structure. The chaotic jerk oscillator hardware circuit was designed and implemented, operating easily with initial conditions set to zero and low DC supply voltages, with all output voltages within ±6V. Both theoretical and simulation results demonstrate the system’s complexity and applicability, with experimental results aligning well with simulations. Consequently, effective microcontroller-based control was achieved using a single-state controller.

A novel family of asymmetric generalized passive voltage‐controlled memristive system

AbstractResearch on the physical memristive system is vital for realizing widespread applications of the memristor in practical engineering. In this article, a family of the asymmetric generalized passive voltage‐controlled memristive system (AGPVCMS) is proposed; also the mathematical model of the AGPVCMS is established and its remarkable memristive characteristics are analysed. Especially, the AGPVCMS only consists of the diode bridge and LR filter, however it can enhance its asymmetry as well as display various asymmetrical pinched hysteresis loops in the voltage–current plane, via expediently changing the number of the diodes on different bridge arms. In addition, the effectiveness of the AGPVCMS is verified by the consistency between theoretical and experimental results.

Resistive Switching Acceleration Induced by Thermal Confinement

AbstractEnhancing the switching speed of oxide‐based memristive devices at a low voltage level is crucial for their use as non‐volatile memory and their integration into emerging computing paradigms such as neuromorphic computing. Efforts to accelerate the switching speed often result in an energy trade‐off, leading to an increase in the minimum working voltage. In this study, an innovative solution is presented: the introduction of a low thermal conductivity layer placed within the active electrode, which impedes the dissipation of heat generated during the switching process. The result is a notable acceleration in the switching speed of the memristive model system SrTiO3 by a remarkable factor of 103, while preserving the integrity of the switching layer and the interfaces with the electrodes, rendering it adaptable to various filamentary memristive systems. The incorporation of HfO2 or TaOx as heat‐blocking layers not only streamlines the fabrication process but also ensures compatibility with complementary metal‐oxide‐semiconductor technology.

(Invited) Engineering the Switching Kinetics of Valence Change-Based Memristive Devices for Neuromorphic Computing

Memristive devices based on the valence mechanism are highly interesting candidates for the use as hardware representation of synapses in neuromorphic computing. The memristive model system SrTiO3 exhibit gradual switching and can be tuned between short-term and long-term plasticity. We will discuss the impact of the switching kinetics on the gradual switching mode and provide general guidelines for the design of gradual switching systems. Moreover, we present an approach to accelerate the switching kinetics of SrTiO3 by up to 103 times, or reduce the operating voltage by ≈ 30% to maintain the switching speed.Our approach is to introduce a low thermal conductivity layer inside the active electrode of the active electrode of the devices, which blocks the heat dissipation caused by Joule heating during switching. Our method leaves the switching layer and its interfaces with the electrodes intact, while the use of HfO2 and TaOx as the heat blocking layers ensures ease of fabrication and CMOS compatibility. We will demonstrate that this approach is transferable to other more common material systems.

Automatic Learning Rate Adaption for Memristive Deep Learning Systems.

As a possible device to further enhance the performance of the hybrid complementary metal oxide semiconductor (CMOS) technology in the hardware, the memristor has attracted widespread attention in implementing efficient and compact deep learning (DL) systems. In this study, an automatic learning rate tuning method for memristive DL systems is presented. Memristive devices are utilized to adjust the adaptive learning rate in deep neural networks (DNNs). The speed of the learning rate adaptation process is fast at first and then becomes slow, which consist of the memristance or conductance adjustment process of the memristors. As a result, no manual tuning of learning rates is required in the adaptive back propagation (BP) algorithm. While cycle-to-cycle and device-to-device variations could be a significant issue in memristive DL systems, the proposed method appears robust to noisy gradients, various architectures, and different datasets. Moreover, fuzzy control methods for adaptive learning are presented for pattern recognition, such that the over-fitting issue can be well addressed. To our best knowledge, this is the first memristive DL system using an adaptive learning rate for image recognition. Another highlight of the presented memristive adaptive DL system is that quantized neural network architecture is utilized, and there is therefore a significant increase in the training efficiency, without the loss of testing accuracy.

Dynamics of memristive Liu system and its DSP implementation

In this paper, a novel four-dimensional memristive system is investigated to generate abundant dynamical behaviors. By combining the Liu chaotic system with an ideal memristor, an enhanced chaotic system is proposed. Dynamical analysis indicates that the new system sustains stable chaotic states and exhibits complex behaviors, with the help of the Lyapunov exponents, bifurcation diagrams, Poincaré section, parametric offset boosting, and SE complexity. The coexistence of attractors is investigated by the variation of parameters. The chaotic performance is enhanced in the proposed system, broadens the range of parameters for chaotical oscillations, and transforms periodic and quasiperiodic states into chaos. The practical applicability and feasibility of the system are validated via NIST testing and DSP implementation. The system exhibits resilient dynamical characteristics that make it highly suitable for deployment in various domains, including secure communication and signal detection.

Cross-Channel Color Image Encryption Scheme Based on Discrete Memristive Coupled Neurons and DWT Compression

To address the consumption and security of color images for transmission and storage, a cross-channel color image encryption scheme based on a discrete memristive coupled neuron model and DWT compression is designed in this article. Firstly, the dynamics of the discrete memristive coupled neuron system are analyzed and found to possess the hyperchaotic phenomenon, which provides sufficient security for the encryption scheme. Secondly, the color image processed by discrete wavelet transform (DWT) has a quarter of the previous capacity. Then, the color image is combined with a Hash function, and the resulting Hash sequence is given the initial value of the hyperchaotic system. Next, a particle swarm foraging algorithm (PSFA) is designed to better disrupt the correlation in the RGB channel. Finally, a complementary DNA coding rule is implemented for the further encryption of color images. Simulation results show that even with DWT lossy compression, the recovered image can be clearly seen. The performance analysis illustrates that under the hyperchaotic system, the proposed encryption algorithm brings higher security for color images.

A new four-dimensional memristive system, synchronization and its application in image encryption

A new four-dimensional memristive system, synchronization and its application in image encryption

A memristive chaotic system with two dimensional offset boosting and extreme multistability

Due to its unique nonlinearity and memory characteristics, memristor is considered one of the most promising partners for designing chaotic systems. In this paper, a memristor is introduced into a nonlinear system to produce complex dynamical behaviors. Symmetric extremely multistability induced by the initial condition of the memristor is observed in the asymmetric system. Attractors are arranged in the phase space by two independent offset boosters, strength cancellation gives birth to various offset boosting patterns. The effective action of the offset controller is reflected in the linear growth of the mean values and the linear shift of the signal diagrams. Moreover, the circuit implementation based on Multisim demonstrates consistency with numerical simulations and theoretical analyses. Finally, the Pseudorandom Number Generator (PRNG), tested through NIST, is developed to validate its high performance in engineering applications.

An ultra high-endurance memristor using back-end-of-line amorphous SiC

Integrating resistive memory or neuromorphic memristors into mainstream silicon technology can be substantially facilitated if the memories are built in the back-end-of-line (BEOL) and stacked directly above the logic circuitries. Here we report a promising memristor employing a plasma-enhanced chemical vapour deposition (PECVD) bilayer of amorphous SiC/Si as device layer and Cu as an active electrode. Its endurance exceeds one billion cycles with an ON/OFF ratio of ca. two orders of magnitude. Resistance drift is observed in the first 200 million cycles, after which the devices settle with a coefficient of variation of ca. 10% for both the low and high resistance states. Ohmic conduction in the low resistance state is attributed to the formation of Cu conductive filaments inside the bilayer structure, where the nanoscale grain boundaries in the Si layer provide the pre-defined pathway for Cu ion migration. Rupture of the conductive filament leads to current conduction dominated by reverse bias Schottky emission. Multistate switching is achieved by precisely controlling the pulse conditions for potential neuromorphic computing applications. The PECVD deposition method employed here has been frequently used to deposit typical BEOL SiOC low-k interlayer dielectrics. This makes it a unique memristor system with great potential for integration.

Adaptive control for memristive system via compensatory controller and Chebyshev neural network

In this paper, based on linear matrix inequality technique, a simple controller and a compensatory controller are designed. It can track arbitrary fixed points and any periodic orbits. In addition, a synchronization control method via Chebyshev neural network with external disturbances is proposed. An adaptive controller is given. The Chebyshev neural network is used to approximate the uncertain nonlinear function and the adaptive law is used to adjust the corresponding parameters in the system. Taking a 4D memristive chaotic system as examples, the results are in consistent with the simulations. From a framework and control theoretical point of view, the proposed synchronization approach via compensation controller and Chebyshev neural network is firstly presented. From an application point of view, the proposed scheme can simplify the complexity of controller design. It is promising in many applications for mem-systems as secure communications and neural networks.

Information Dimension Matching in Memristive Computing System for Analog Deployment of Deep Neural Networks

AbstractMemristor, with the ability of analog computing, is widely investigated for improving the computing efficiency of deep neural networks (DNNs) deployment. However, how to fully take advantage of the analog computing ability of memristive computing system (MCS) for DNN deployment is still an open question. Here, a new neural network models deployment scheme, that is, an information dimension matching (IDM) scheme, is proposed to fully take advantage of the analog computing ability of MCS. Furthermore, the spatial and temporal DNN, that is convolutional neural network (CNN) and recurrent neural network (RNN) is used to verify the proposed deployment scheme, respectively. The experimental results indicate that, compared to the traditional deployment schemes, the proposed deployment scheme shows obvious inference accuracy and energy efficiency improvement (>4 × in four‐layer DNNs deployment), and the energy efficiency improvement increases dramatically with the layers increment of DNNs. This work paves the path for developing high computing efficiency analog MCS.

Integrated circuit design of a discrete memristive chaotic system optimized by the fixed-point specific processor with acceleration instructions

Integrated circuit design of a discrete memristive chaotic system optimized by the fixed-point specific processor with acceleration instructions